Pfizer

This role focuses on designing, building, and deploying production-grade AI systems, particularly LLMs and agentic AI, for clinical development and operations within a regulated healthcare environment. It involves developing predictive models, engineering robust ML pipelines, and implementing agentic solutions, with a strong emphasis on MLOps and cloud deployment.

This role leads the strategy and technical direction for applied AI in Inflammation & Immunology at Pfizer, focusing on translating advances in LLMs, agentic systems, and multimodal AI into reusable capabilities. The position involves shaping the AI portfolio, defining governance and evaluation standards, managing external partnerships, and driving AI adoption across scientific decision-making processes. It requires a technically credible leader with strong scientific judgment to ensure solutions are robust, trusted, and improve the speed and quality of evidence generation.

Senior Manager role focused on designing, developing, and deploying AI agents for enterprise workflow automation, decision enhancement, and intelligent user interactions. Requires experience with agentic orchestration frameworks, vector databases, RAG, and LLM principles, along with leadership in managing technical teams and delivering enterprise-scale projects.

This role focuses on building and deploying production-grade full-stack applications that integrate LLM and AI capabilities into pharmaceutical research workflows. Responsibilities include developing backend services for data processing, embedding generation, vector search, and LLM orchestration, creating frontend interfaces, implementing RAG systems and agentic LLM architectures, and deploying/maintaining systems on AWS. The role also involves contributing to semantic frameworks and conceptual research.

Senior Manager, Applied AI Workflow Scientist at Pfizer, focused on applying LLMs and agentic AI to scientific use cases in Inflammation & Immunology. The role involves identifying high-value scientific applications, building reusable AI workflows and agentic systems, and partnering with scientists to ensure adoption and impact. It emphasizes practical application, scientific credibility, user partnership, and scalable workflows.